{
 "cells": [
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-09T14:17:29.737882Z",
     "start_time": "2025-04-09T14:17:29.733688Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets import fetch_openml\n",
    "from sklearn.linear_model import Perceptron\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import (accuracy_score, precision_score,\n",
    "                            recall_score, f1_score, confusion_matrix)\n",
    "import seaborn as sns\n",
    "import time"
   ],
   "id": "576f581822691811",
   "outputs": [],
   "execution_count": 15
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-09T14:17:24.664303Z",
     "start_time": "2025-04-09T14:17:21.341721Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 1. 数据准备与预处理\n",
    "print(\"正在加载MNIST数据集...\")\n",
    "mnist = fetch_openml('mnist_784', version=1, as_frame=False)\n",
    "\n",
    "# 修正部分：通过字典键提取数据和标签\n",
    "X, y = mnist.data, mnist.target  # 关键修正点\n",
    "y = y.astype(np.uint8)"
   ],
   "id": "f11c05affaaef880",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "正在加载MNIST数据集...\n"
     ]
    }
   ],
   "execution_count": 2
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-09T14:17:26.130181Z",
     "start_time": "2025-04-09T14:17:25.314431Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 数据标准化\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)"
   ],
   "id": "b84d76d8ac04b4a6",
   "outputs": [],
   "execution_count": 3
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-09T14:17:27.151999Z",
     "start_time": "2025-04-09T14:17:26.139361Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 划分训练集和测试集\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)"
   ],
   "id": "cd8f8de98392f7e0",
   "outputs": [],
   "execution_count": 4
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-09T14:17:27.172217Z",
     "start_time": "2025-04-09T14:17:27.167176Z"
    }
   },
   "cell_type": "code",
   "source": [
    "print(f\"训练集大小: {X_train.shape}\")\n",
    "print(\"数据预处理完成.\\n\")"
   ],
   "id": "e094a2dfd8027fe5",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集大小: (56000, 784)\n",
      "数据预处理完成.\n",
      "\n"
     ]
    }
   ],
   "execution_count": 5
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-09T14:17:27.219180Z",
     "start_time": "2025-04-09T14:17:27.213603Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 2. 调用sklearn实现感知机模型\n",
    "print(\"开始训练感知机模型...\")\n",
    "start_time = time.time()"
   ],
   "id": "3e0466989b84ba52",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "开始训练感知机模型...\n"
     ]
    }
   ],
   "execution_count": 6
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-09T14:17:27.264472Z",
     "start_time": "2025-04-09T14:17:27.258274Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 创建感知机模型\n",
    "# 设置max_iter和tol以避免收敛警告\n",
    "#增加学习率和正则化参数\n",
    "perceptron = Perceptron(max_iter=2000, tol=1e-4, eta0=0.1, penalty='l2', random_state=42, n_jobs=-1)"
   ],
   "id": "f0a95f3a72d5f1ab",
   "outputs": [],
   "execution_count": 7
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-09T14:17:28.833172Z",
     "start_time": "2025-04-09T14:17:27.281825Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 训练模型\n",
    "perceptron.fit(X_train, y_train)\n",
    "training_time = time.time() - start_time"
   ],
   "id": "b1b9f10c8e45bff4",
   "outputs": [],
   "execution_count": 8
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-09T14:17:29.028907Z",
     "start_time": "2025-04-09T14:17:28.843199Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 预测\n",
    "y_pred = perceptron.predict(X_test)"
   ],
   "id": "5229ce36c897f7a2",
   "outputs": [],
   "execution_count": 9
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-09T14:17:29.061546Z",
     "start_time": "2025-04-09T14:17:29.038940Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 3. 模型评估\n",
    "# 计算各项指标\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "precision = precision_score(y_test, y_pred, average='macro')\n",
    "recall = recall_score(y_test, y_pred, average='macro')\n",
    "f1 = f1_score(y_test, y_pred, average='macro')\n",
    "conf_mat = confusion_matrix(y_test, y_pred)"
   ],
   "id": "d5183e5067f806b1",
   "outputs": [],
   "execution_count": 10
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-09T14:17:29.078004Z",
     "start_time": "2025-04-09T14:17:29.072927Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 打印性能指标\n",
    "print(\"\\n模型性能指标:\")\n",
    "print(f\"- 准确率(Accuracy): {accuracy:.4f}\")\n",
    "print(f\"- 精确率(Precision): {precision:.4f}\")\n",
    "print(f\"- 召回率(Recall): {recall:.4f}\")\n",
    "print(f\"- F1分数(F1-score): {f1:.4f}\")\n",
    "print(f\"- 训练时间: {training_time:.2f}秒\")"
   ],
   "id": "1894a50e88ebada7",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "模型性能指标:\n",
      "- 准确率(Accuracy): 0.8627\n",
      "- 精确率(Precision): 0.8619\n",
      "- 召回率(Recall): 0.8604\n",
      "- F1分数(F1-score): 0.8603\n",
      "- 训练时间: 1.61秒\n"
     ]
    }
   ],
   "execution_count": 11
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-09T14:17:29.689869Z",
     "start_time": "2025-04-09T14:17:29.099802Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 4. 可视化混淆矩阵\n",
    "plt.figure(figsize=(10, 8))\n",
    "\n",
    "# 设置支持中文的字体\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 使用黑体\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 正确显示负号\n",
    "\n",
    "sns.heatmap(conf_mat, annot=True, fmt='d', cmap='Blues',\n",
    "            xticklabels=[str(i) for i in range(10)],  # 将标签转换为字符串\n",
    "            yticklabels=[str(i) for i in range(10)])  # 将标签转换为字符串\n",
    "\n",
    "plt.xlabel('预测标签', fontsize=12)  # 设置字体大小\n",
    "plt.ylabel('真实标签', fontsize=12)  # 设置字体大小\n",
    "plt.title('感知机模型混淆矩阵', fontsize=14)  # 设置标题字体大小\n",
    "plt.xticks(rotation=45, ha='right')  # 旋转x轴标签，避免重叠\n",
    "plt.yticks(rotation=0)  # y轴标签保持水平\n",
    "plt.tight_layout()  # 自动调整子图参数，使之填充整个图像区域\n",
    "plt.show()"
   ],
   "id": "5806f402f0a37920",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 12
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
